System and method for inferring geological classes

ABSTRACT

A system for inferring geological classes from oilfield well input data is described using a neural network for inferring class probabilities and class sequencing knowledge and optimising the class probabilities according to the sequencing knowledge.

FIELD OF THE INVENTION

This invention relates the enhancements of neural network-assistedreservoir characterization techniques for geological classification frommeasured input data.

According to the present invention, the terms “measured input data” or“INPUT DATA” refers to, in particular, downhole logs. The set of logsused in the testing of the method of the invention includes gamma ray(GR), sonic slowness (DT), thermal neutron porosity (NPHI), bulk density(RHOB) and true resistivity (RT), all measured at same depth for eachsample, and at a constant sampling distance. However, INPUT DATA are notrestricted to samples at a single depth. Alternatively, attributes thatrepresent, for example, sliding window averages or other statisticstaken over a depth range in the neighborhood of the depth of interest,can be constructed. 2D image logs (e.g., FMI) or 3D seismic cubes arealso encompassed.

According to the present invention, the terms “geological classes” or“CLASSES” refers to, principally, the rock facies (lithofacies) or thereservoir rock types. However, any other discrete classification ofgeological features (e.g. petrophysical properties) is possible.

PRIOR ART

Rock facies class prediction by neural network processors applied todownhole logs is an existing method developed in the nineteen ninetieswhich gave rise to several publications [1]. For instance, it has beenimplemented by an ENI AGIP E&P team, and integrated in a jointdevelopment project into the product RockCell™ within the Schlumberger™GeoFrame™ oilfield interpretation software platform.

For rock facies estimation, a set of single-channel log curves areselected. Typical logs used are gamma ray (GR), sonic slowness (DT),thermal neutron porosity (NPHI), and bulk density (RHOB), but this listis not limited. New attributes can also be generated from existing logsin order to reveal additional features in the logs.

A current limitation in analyzing geological measured data such asdownhole logs, is that their relationship to classes such as rock faciesis not obvious. In each borehole, there are unknown local factors thatmay affect the data in unexpected ways. It can thus be risky to classifyon a simplified theoretical analysis or by data clustering. There is aneed for a method to identify associations between input data and tobuild implicit complex functional relationships. A “learn from examples”method is more preferred to building an expert system. The discoveredmethods would then be used to predict the classes and their associatedprobabilities.

An Artificial Neural Network (ANN) scheme has been developed toimplement learning by example as applied to downhole geologicalclassification. Neural networks can “learn” specific computationschemes. Once trained, a neural network can find acceptable solutions onany set of data referring to the learned schemes. This gives artificialneural networks an ability to generalize from training experience (see[12]). Unlike analytical approaches such as statistics, neural networksrequire no explicit computational model, and are not limited by a lackof normality or the non-linearity of the physical phenomenon. As aconsequence, they “learn” relationships between data that may be hard todiscover with analytical methods.

The behavior of a neural network is defined by its architecture. Thisarchitecture consists of the way its neurons (individual computingelements) are connected and by strength (weight) of those connections.Each neuron performs a weighted sum (linear combination) of its inputs,then applies an almost non-linear activation function, to finallyproduce an output. The resulting output of a given neural layer isforwarded to the next layer and so on through the network. In otherwords, neural networks plainly perform a massively parallel set ofelementary computations. Whereas the weights vary the strength ofconnections from one node to another, the sigmoidal activation functionprovides the highly non-linear property of neural data processing.

The main advantage of those neural nets is their learning capability.During the learning phase, given a training set of data, theinterconnection weights are gradually adjusted so as to stabilize thenetwork's output, and, in the case of the supervised learning, tominimize the mean square error between the effective output and thedesired one. The preferred implementation of the NN is a supervisedfeed-forward, multi-layer perceptrons trained with the back-propagationalgorithm.

Methods and techniques used today are able to classify without the apriori knowledge of classes sequencing. The prediction operates ongeological input data sample-by-sample, and produces for each inputpattern the probabilities of the most likely classes.

However, this system sometimes fails in its predictions. One of its mainlimitations is that it does not honour geological prior knowledge. Someof the predictions fail due to the fact that geologically improbableclasses transitions are often observed.

Sedimentologists have observed that the vertical and lateral sequence ofgeological facies¹ seen in outcrop and in the subsurface are not random.Since the stratigraphic layering in the earth represents successive timeof deposition, the rock record actually represents a time series ofevents. Since the normal neural net techniques make sample-by-samplepredictions, they do not consider previous states of prediction (e.g.,the facies predicted at location X_(n-1), which implies t_(n-1),constrains the prediction at location X) and they fail to take advantageof likely non-random transitions between lithology or facies. Geologycan provide strong constraints on the prediction of stratigraphicsuccessions. Sedimentologists have long invoked Markov models foranalyzing the vertical and lateral sequences [2, 3, 4, 5]. Therefore,using a Markov scheme using geological prior information of rock faciestransition probabilities seems a fruitful way to improve the predictionof the neural network scheme.

Systems for speech recognition, integrating a neural network and aHidden Markov Model (HMM), are known from the state of the art. HMMs areused as a major approach in the majority of continuous speechrecognition systems. They provide an accurate and reliable framework forsegmentation and classification of speech. HMM states can stand for thephone classes, c_(i) (e.g., phonemes) to be identified, whereas the HMMobservation sequence for the acoustic vectors y (e.g., a combination ofcepstral and energy acoustic parameters). As a consequence, the statesequence X=x₁, x₂, . . . , x_(T) of length T can be considered as the,“sentence” to be recognized due to the recorded and discretized acousticobservation sequence Y=y₁, y₂, . . . , y_(T).

Facies sequences have been considered as analogous to the phonemesequences in the speech recognition methods. The HMM and its stochasticbehavior represent the allowed or forbidden transitions betweengeological classes and their associated probabilities, and thegeological input data are analogous to the acoustic observation vectorsused during the speech recognition process.

The HMM technology has already been applied to lithofaciesclassification from well logs. Publications [6], [7] and [8] describethe building, training and application of a Hidden Markov Model toestimate the lithology of uncored boreholes based on key learning datasets where the lithology is known. In those methods, the lithofaciessequence stands for the consecutive states of the HMM, and the log datafor the observations. Those methods do not rely on the use of a neuralnetwork. This means they are able to model the stochastic character ofrock facies transitions and the rock facies sequences. However, theyperform poorly while modeling the non-linear relationship between logsand rock facies, as they do not benefit from the complex neural networkarchitectures and computation schemes.

In the papers [9] and [10], and in several patents concerning speechrecognition, such as [11], an interesting approach to classify speechphonemes has been developed by the use of hybrid models mixing both HMMand ANN. Those approaches enable speech recognition systems to cope withthe strong statistical assumptions of the HMMs.

Applying a feed-forward neural network to the input data y can give usestimates of the conditional posterior probabilities p(x_(i)|y) of eachclass x_(i), given the current input vector y.

Those class-conditional-posterior probabilities must sum to one, andtherefore need to be normalized. However, a HMM needs the conditionalprior probabilities p(y|x_(i)). Assuming there are enough training dataand that the training does not get held up in poorly performing localminima, the feed-forward neural network is able to approximate the priorprobabilities thanks to Bayes' rule. Indeed, p(y|x_(i))=p(x_(i)|y) xp(y)|p(x_(i)). The prior probability distribution of classes iscontext-dependent but can be estimated by counting the classes occurenceof classes in the learning set, or by introducting prior knowledge. Theprior probability of the observation vector can be discarded as for eachtime step; it is independent of the phone class.

The HMM and observation sequence finally provide, thanks to the Viterbialgorithm, the most likely state sequence which caused the observedacoustic data sequence.

REFERENCES CITED

-   [1] Hall J., Scandella L. (1995). “Estimation of Critical Formation    Evaluation Parameters Using Techniques of Neurocomputing”, Society    of Professional Well Log Analysts Annual Logging Symposium, 36th,    Paris, France, 1995, Transactions, p. PPP1-PPP12.-   [2] Gingerich, P. D. (1969). “Markov analysis of cyclic alluvial    sediments.” Journal of Sedimentary Petrology 39: 330-332.-   [3] Miall, A. D. (1973). “Markov chain analysis applied to an    ancient alluvial plain succession.” Sedimentology 20: 347-364.-   [4] Carr, T. R. (1982). “Log-linear models, markov chains and cyclic    sedimentation.” Journal of Sedimentary Petrology 53(2): 905-912.-   [5] Powers, D. W., Easternling, R. G. (1982). “Improved methodology    for using embedded Markov chains to describe cyclical    sedimentation.” Journal of Sedimentary Petrology 52(3): 913-923.-   [6] Eidsvik, J., Mukerji, T, Switzer P. (2002). “Estimation of    geological attributes from a North Sea well log: an application of    hidden Markov chains”, Norges Teknisk—Naturvitenskapelige    Universitet, submitted for publication in 2002.-   [7] Schumann A. (2002). “Hidden Markov Models for Lithological Well    Log Classification”, Freie Universitat Berlin. Presented at the    Annual Conference of the International Association for Mathematical    Geology, 2002.-   [8] Padron, M., Garcia-Salicetti, S., Barraez, D., Dorizzi, B.,    Thiria, S. (2002). “A Hidden Markov Model Approach For Lithology    Identification From Logs”, Institut National des Telecommunications    Evry, Univesidad Central de Venezuela Caracas, Universite Pierre et    Marie Curie Paris. Submitted for the 3rd Conference on Artificial    Intelligence Applications to the Environmental Science, 83^(rd)    Annual Meeting of American Meteorological Society, 2003.-   [9] Srivastava S. (2001). “Hybrid Neural Network/HMM Based Speech    Recognition”, Department for Electrical and Computer Engineering,    Mississippi State University, 2001.-   [10] Renals, S., Morgan, N., Bourlard, H., Cohen, M., Franco, H.    (1994). “Connectionist Probability Estimators in HMM Speech    Recognition”. IEEE Trans. Speech and Audio Processing, 2:161-175,    1994.-   [11] Albesano, D., Gemello, R., Mana, F., (1996). “Speaker    independent isolated word recognition system using neural networks”,    U.S. Pat. No. 5,566,270, Oct. 15, 1996.-   [12] Bishop C. (1995). Neural Networks for Pattern Recognition,    Oxford Press 1995.

SUMMARY OF THE INVENTION

The starting point of this invention consists of enhancing the neuralnetworks algorithms to make their predictions more accurate and robustin oilfield applications.

In a first aspect, the invention concerns a system for inferringgeological classes from oilfield well input data comprising a neuralnetwork for inferring class probabilities, characterized in that saidsystem further comprises means for integrating class sequencingknowledge and optimising said class probabilities according to saidsequencing knowledge.

Preferably, the means for integrating class sequencing knowledge andoptimising said class probabilities according to said sequencingknowledge comprises a hidden Markov model.

In a second aspect, the invention concerns a method for inferringgeological classes from oilfiled well input data, comprising thefollowing steps:—inferring class probabilities with a neural network;and—integrating class sequencing knowledge and optimising said classprobabilities according to said sequencing knowledge.

Preferably, integrating class sequencing knowledge and optimising saidclass probabilities according to said sequencing knowledge is achievedaccording to a hidden Markov model.

Advantageously, the invention relates to a system and method forinferring geological classes from single-channel oilfield input data byapplying hybrid neural network hidden Markov models classifiers.

The geological classification is inferred using supervised neuralnetworks that are applied to the input data and that predict theassociated classes. The vertical class transition constraints arelearned within a Markov class transition table and a prior classdistribution, which are then reused during the estimation of theclasses. This optimizes the predicted class curve and honours geologicalprior knowledge.

This invention relates the enhancements of artificial neural network(ANN) reservoir characterization techniques for geologicalclassification. Supervised neural network classifiers can be applied todownhole logs to automatically predict lithology or other classes inboreholes. However, ANN systems sometimes infer geologically incorrectvertical (stratigraphic) class transitions within a borehole. The rootcause of these errors is the fact the networks analyze and predict theoutput classes sample-by-sample, without taking the whole boreholesequence of classes into account. Improving the prediction oflithofacies from downhole logs is solved by the system and method of thepresent invention. In essence, they do not take into account localinformation that is commonly important in stratigraphic rock sequences.Geological transitions are commonly not random, but predictablysequenced.

The system integrates an a priori knowledge of class sequencing and ofclass probability distribution in the neural network predictor. Itcombines a supervised back-propagation, feed-forward neural networkarchitecture with a Hidden Markov Model module into a complex hybridneural processing chain. The second processing step optimizes the class,stratigraphic sequence. Instead of simply choosing, for each set ofinput data the class that is the most probable, the chosen class is theone which has both a reasonable occurrence probability given the inputdata pattern and a reasonable occurrence probability given the previousestimated class. Such a choice governed both by class transition and aposterior class observation probability is implemented through theViterbi algorithm.

These and other features of the invention, preferred embodiments andvariants thereof, possible applications and advantages will becomeappreciated and understood by those skilled in the art from thefollowing detailed description and drawings.

DRAWINGS

FIG. 1 is a block-diagram of the training of the hybrid ANN-HMMclassification system. The training set consists of INPUT DATA acrossseveral wells and associated core information. The normalization ofINPUT DATA and the generation of additional attritutes is optional(dotted arrows). The construction of the HMM during the training phaseis optional as well, if it is not essential to compute it for thetraining data set.

FIG. 2 is a block-diagram of the estimation of the hybrid ANN-HMMlithofacies classification system on uncored boreholes by applying thesystem to well logs. The normalization of INPUT DATA and the generationof additional attritutes is optional (dotted arrows). As for the HMM,one can load an existing HMM from the data storage system and/ormanually define it on the basis of the geological prior knowledge.

FIG. 3 is a block diagram of the Hybrid ANN-HMM processing chain in thegeological classification estimation mode. The supervised, neuralnetwork module aims to predict the posterior class probabilities givenan observation. The Viterbi processing optimizes the predicted classpath.

FIG. 4 depicts the same process as in FIG. 3, but with a state-to-stateHMM instead of a sample-by-sample one.

FIG. 5 a shows a particular ANN architecture where the neural networkintegrates a Kalman-trained matrix K.

FIG. 5 b illustrates the concept of neural network expert committee.

MODE(S) FOR CARRYING OUT THE INVENTION

The Hybrid ANN/HMM of an example of the invention is composed of twodifferent components, which are the ANN posterior CLASS probabilityestimator, and the HMM, comprising only a CLASS transition table and aCLASS probability distribution. In the present example those componentsare trained separately during the training phase of the system, as theydo not need to interfere with one another during the learning step. Theyare also applied separately during the estimation step.

In the following single steps and components of the example of theinvention are described in greater details making reference to FIG. 1 to5.

1. Data Choice and Input

Processing of the INPUT DATA is done on a sample-by-sample basis, andtherefore the CLASS probabilities are estimated for each sample.

1.1. Borehole Choice (See Step 1.1 to 1.3 on FIG. 1 and 2.2 on FIG. 2)

Both the learning and the estimation of the Hybrid HMM/ANNclassification system can be done on several wells, as long as theyshare the same geological INPUT DATA and properties. This system is byconsequence designed to propagate the knowledge of the physical andstatistical relationships between INPUT DATA and CLASSES, as measured inone or several wells, to the whole set of boreholes within an oilfield.

If one or more INPUT DATA curves are missing, they can be estimatedbased on available data. One can for instance integrate synthetic logsso as to perform rock class estimation.

1.2. Input Data Choice (See Step 1.1 on FIG. 1 and 2.1 on FIG. 2)

The following section log curves are used as INPUT DATA. For thepurposes of testing and validating the method outlined here, the set oflogs used includes gamma ray (GR), sonic slowness (DT), thermal neutronporosity (NPHI), and bulk density (RHOB), all measured at same depth foreach sample, and at a constant sampling distance. A regression is oftendone on those data by taking several samples above and several samplesbelow the depth of interest.

However, any set of 1D, 2D or 3D geological or geophysical informationis suitable for this system. 1D depth-oriented attributes are extractedfrom the existing information. Alternatively sliding window statisticscan be extracted at the neighborhood of the depth of interest, and thiscan be done for instance on 2D open borehole microresistivity images or3D seismic cubes.

1.3. Training Data Set and Cross-Validation Data Set (See Step 1.3 onFIG. 1)

The learning data includes both INPUT DATA and core or geologist-definedcorresponding CLASSES zonation. This CLASSES zonation of the INPUT DATAis considered as the desired goal which has to be attained by the HybridHMM/ANN classification system.

The supervised training of the ANN component is done for each sample ofthe INPUT DATA, and for as many epochs as necessary, until a global meansquare error between the desired outputs and the actual outputs issatisfactory. A second error, the so-called cross-validation error, isalso computed on a different data set, not taken into account whentraining the ANN. This monitors the generalizing abilities of the ANN,preventing the ANN from learning the training data set “by heart”.Usually, the training stops when the cross-validation error starts toincrease.

The separation between training set and cross-validation set is done onthe basis of random choice. The total percentage of data being selectedfor the cross-validation set is chosen during the ANN architecturechoice step, for instance p=50%. Then, for each sample of the INPUT DATAset, that sample is randomly attributed to the training set or thecross-validation set according to the probability p.

1.4. Additional Attributes Generation (See Step 1.4 on FIG. 1 and 2.3 onFIG. 2)

Under certain circumstance, for example if the INPUT DATA does notcontain, on a localized sample-by-sample basis, enough spatialinformation which could help to discriminate among CLASSES, additionalinformation may be extracted from existing data, e.g. seismic data orlogs. That information can for instance, show the evolution of the INPUTDATA curves, the energy contained in the curves or the smoothedlow-frequency component of the curves. In order to obtain suchinformation one can apply a set of gradients to the INPUT DATA curves,or extract low frequencies using for example the Fast Fourier Transform,or approximate the INPUT DATA curves with Polynomial curves on smallwindows, i.e. a subset of the data.

This additional attributes generation is done both for the learning dataset and for the estimation data set.

1.5. Log Data Normalization (See Step 1.5 on FIG. 1 and 2.4 on FIG. 2)

The generalization performance of the ANN can be enhanced using apre-processing step consisting of normalizing of the data. This optionalstep can consist of one or more of the following computations:Mean—Standard Deviation Normalization, Principal Component Analysis(with retention of the principal components which account for 95 or 98%of the data), Mininum—Maximum Normalization or other known methods fordata normalization.

2. Neural Network Component

Several architectures, training algorithms, and methods ofimplementation are possible for the neural network. In the presentexample the component is a feed-forward MLP (Multi-Layer Perceptron),with an input layer (one neuron per log data attribute), one or severalhidden layers, and an output layer (one neuron per CLASS). The outputsO=(o₁, o₂, . . . o_(N)) of the ANN have to be the probabilities of eachCLASS, according to the current INPUT DATA, and as a consequence have toequal 1 and each belong to the interval [0, 1].

2.1. Choice of the Neural Network Architecture (See Step 1.6 on FIG. 1and FIGS. 5 a, 5 b)

A preferred embodiment of this method is the three-layered neuralnetwork, with: sigmoid activation functions; bias; as many nodes on thefirst layer as there are log attribute inputs, for instance 4, then 10nodes on the first hidden layer and 10 nodes on the second hidden layer.The number of nodes on the output layer is the same the number ofCLASSES.

As illustrated in FIG. 5A, an additional linear matrix K can be added tothe ANN after the output layer of the neural network. In this case, thelast neural layer does not need to have as many nodes as there areCLASSES, but the linear matrix K has to be correctly sized andperforming the following operation: X=K Xh, where X is a vector of sizeN (N being the number of CLASSES), Xh is a vector of size Nh coming outfrom the last neural layer, and K is a matrix of size N×Nh.

Independent of which ANN architecture is retained, several ANN modulescan be combined into a neural network “expert committee” as shown onFIG. 5 b, step 5 b.2.

After selection of the ANN architecture the neural networks are trained.

2.2. Training the ANN (See Step 1.7 to 1.10 on FIG. 1)

2.2.1. Evaluation of the Network Performances at Each Step (See Step 1.8on FIG. 1)

Evaluation is realized by computing the global RMSE (Residual MeanSquare Error) between the desired outputs as provided by the training orthe cross-validation data set, and the actual outputs of the ANN. Twocurves corresponding to that training and cross-validation error aredisplayed and monitored during the training process.

2.2.2. Error Back-Propagation (See Step 1.7 on FIG. 1)

The supervised training of the neural network is performed by the ErrorBack-Propagation, and the algorithm used can be, for instance, GradientDescent with Adaptative Learning Rate and Momentum. This means that thedifference between the expected CLASS probability as provided by thetraining data set, and the actual current output of the ANN, ispropagated backwards through the ANN and the neural weights areaccordingly updated. The Adaptative Learning Rate means that thiscorrection is proportional to a learning rate which is tuned accordinglyto the evolution of the global RMSE. The Momentum means that a termcorresponding to the total sum of the neural weights of the network isadded to that global error, with the aim of avoiding the values of thoseweights increasing too much.

2.2.3. ANN Expert Committees (See FIG. 5 b)

Instead of one ANN, one can run several ANN and the average of theiroutputs (see step 5 b.3)can be taken as CLASS probability. The trainingof each ANN module of that committee is done using to a bootstrapprocedure (see step 5 b.1), which includes the steps of slightlyaltering the training set for each ANN (different partition of INPUTDATA samples between the training set and the cross-validation set), andrandomly initializing the neural weights of the ANN before training. Thegeneralization abilities of the ANN are then enhanced.

2.2.4. Training of the K Matrix by Kalman Filtering (See FIG. 5 a)

In cases described above where a linear matric is used to associated thelast layer output with the CLASS output, this K matrix is trained inaddition to the neural network.

This K matrix is trained in the following way:—At each epoch of thetraining, a first run of the ANN through all the INPUT DATA samples isrealized.

-   -   As a result a matrix Mh is computed. Each row Xh of that matrix        corresponds to the outputs of the last layer of the ANN, for a        given INPUT DATA sample.    -   The training data set is a matrix Mt where each row corresponds        to the CLASS probabilities Xt for a given INPUT DATA sample    -   The matrix K is approximated by a Kalman-Filtering technique so        as to minimize the RMSE of E=Xt−K Xh where E and Xt are vectors        of size N (N being the number of CLASSES).    -   Once the matrix K is approximated, the Back-Propagation is        applied to the ANN for all the training data set samples, and        the error is propagated through K first before being propagated        through the network.

After these steps the training is completed.

2.2.5. Termination of the Training (See Step 1.9 and 1.10 on FIG. 1)

The end-user sets the number of training epochs for the NN. The trainingmight stop earlier if the cross-validation error has begun to increase.If the final performances of the ANN are not satisfactory, the end-usercan tune the ANN parameters and try a different configuration. One canalso envisage a system where several configurations of the ANN aresuccessively automatically tried and the best one retained.

A further component of the system is a Hidden Markov Model. A HMM isused to optimize the class stratigraphic sequence by choosing, for eachset of input data a class that is the most probable and which has both areasonable occurrence probability given the input data pattern and areasonable occurrence probability given the previous estimated class.

3. The Hidden Markov Model Component

3.1. Training of the HMM

CLASS and CLASS transistion is preferably based on prior geologicalknowledge derived for example from core evaluation or evaluation of logdata by experts. The knowledge is split into a CLASS probabiltydistribution and CLASS transistion probabilities.

3.1.1. Automatic Training on the Cored/Geologist Defined LithofaciesData (See Step 1.11 on FIG. 1)

The CLASS transition probability table depends only on the CLASSES ofthe learning set and it is therefore an absolute and static reference.It is computed by counting the successive CLASS transitions. It cannotbe influenced by the neural predictions, and for a proper application ofthe Viterbi algorithm, it should be learnt on a large training set offacies log curves. It is possible to learn the CLASS transition table ona set of multiple wells, and in this case the CLASS transitions betweentwo different wells are obviously not taken into account in thecomputation of the CLASS transition table.

These CLASS transitions can be counted on a sample-by-sample basis,(i.e. for each INPUT DATA sample), or on a state-to-state basis,grouping all the samples from the same CLASS together (see FIG. 4).

A similar automatic computation is done to approximate the CLASSprobability distribution.

3.1.2. Geologist-Driven Correction of the HMM Model (See Step 1.12 onFIG. 1, 2.5 on FIG. 2, and 3.1 on FIG. 3)

This correction is performed based on the geologist's expert priorknowledge and can be done after the automatic estimation of the HMM onthe learning data set, or before applying the HMM to a specificestimation data set of borehole logs. It relies on the CLASS transitionprobabilities, (and) the CLASS probability distribution. In case of rockfacies classification, it can also rely on the lithofacies bed thicknesswhich the geologist has defined in his geological study.

After the learning or training epoches probabilities derived from theHMM are combined with the neural network to generate a CLASS estimate.In the present example Bayesian statistics is used to estimate the CLASSbased on availiable knowledge.

3.2. Combining HMM and ANN

3.2.1. Applying Bayes' Rule (See Step 3.2 to 3.4 on FIG. 3 and 2.6 to2.7 on FIG. 2)

The ANN posterior CLASS probabilities estimator is designed to workindependently from the observation set. Actually, this is a plainclassifier, providing the probability p(x_(i)) for each INPUT DATAsample. However, this can also be expressed as the posterior CLASSprobabilities for each input data pattern given the current observation,p(x_(i)|y).

A HMM model makes use of three different elements, which are: the statetransition probabilities matrix, the state probability distribution, andthe observation probability matrix given the current state p(y|x_(i)).Those three elements are also required for the Viterbi algorithmdescribed below.

In order to get p(y|x_(i)), one needs to apply the Bayes Rule and tointroduce the observation (INPUT DATA) probability distribution and thestate (CLASS) probability distribution. However, as the INPUT DATAobservations are continuous, and as for each depth step of the Viterbialgorithm that observation probability remains the same for all thepossible CLASSES, one can just discard the p(y) value.

3.2.2. Applying the Viterbi Optimization Algorithm (See Step 3.5 on FIG.3 and 2.8 on FIG. 2)

At this stage, the hybrid ANN/HMM classification system has both a priorCLASS distribution, a CLASS transition probabilities matrix, and theposterior CLASS probabilities matrix, which depend on the time patternsand the observations. The Viterbi algorithm can be applied to thosedata, provided that the application of Bayes' rule will transform theposterior CLASS probabilities into prior observation probabilities giventhe previous CLASS and the time pattern.

It can be seen that there is no need to compute the markovian matrix ofthe observation probabilities given the CLASS. The Viterbi algorithm cantherefore directly integrate the time-dependent observationprobabilities given the current CLASS and the current time pattern. Inother words, the state transition matrix and the state probabilitydistribution have a static behavior (although they can be tuned to thecontext of the estimation) whereas the observation probabilities, giventhe previous CLASS, are depth-dependent.

3.2.3. State-To-State or Sample-By-Sample Classes Transitions (See FIG.4)

In case of sample-by-sample classes transitions, the Viterbi algorithmis applied to all the INPUT DATA and associated estimated CLASSESprobabilities samples.

In case of state-to-state transitions, all the consecutive samples whichhave in common the same most probable CLASS (see step 4.1), are groupedtogether (see step 4.2) and considered as an unique observation element.The CLASS probabilities of all the samples belonging to that element arealso averaged (see step 4.3), and the Viterbi algorithm is then appliedto the groups of observations and not to each observation sample.

The mean of computing the CLASS probabilities of the observation elementcan be either a plain mathematical average or a more complex average.

In order to perform a state-to-state Viterbi optimization, the statetransition probabilities and the state distribution have also to becomputed on a state-to-state basis and not on a sample-by-sample basis(see 3.1.1 and step 4.4 on FIG. 4).

After the Viterbi optimization, the observation elements are split intoINPUT DATA samples again, and the CLASS curve is displayed on asample-by-sample basis (see step 4.5 on FIG. 4).

4. Testing results

The simple ANN and the Hybrid ANN/HMM classifiers have been trained andtested on three different sets of geological data. As input datadownhole logs were used, and as classification results, the rock faciesclasses.

4.1. Data Set 1 (Cored Logs)

The first data set used for the training contained 490 samples of 4 logs(DT, GR, NPHI and RHOB) and associated core facies (7 facies classes), rand were taken from real measurements performed in a well between thedepth of 2975-3051 m.

Once trained, the hybrid system has been tested on the measurements fromthe same well between depths 2923-3161 m, which corresponded to 1562samples from the same logs.

Whereas a single ANN system showed an amount of approximately 40 to 45%correct predictions, the hybrid ANN/HMM system reached an accuracy of 45to 55%.

4.2. Data Set 2 (Cored Logs)

The data set used for the training contained 3800 samples of 5 logs (DT,GR, NPHI, RT and RHOB) and associated core facies (13 facies classes),and were taken from real measurements performed in 4 wells between thedepth of 8000-9000 feet.

Once trained, the hybrid system has been tested on the measurements from4 other wells of the same field, where core data were available. Theaccuracy of the results has been significantly increased.

4.3. Data Set 3 (Non-cored Logs)

In this example, the training data set contained about 2000 samples fromone well. The testing data set contained between 1500 and 2000 samplesper well in a field of 5 wells.

The lithofacies learning set for the Hybrid ANN/HMM system was providedby the results of electrofacies predictions of an unsupervised neuralnetwork classifier. The stability of the predictions of the lattersystems has then been compared with the predictions of a plain ANNtrained on the same lithofacies log curve.

The Hybrid ANN/HMM system is more reliable than a single ANN system inthe terms of prediction accuracy and gave less noisy results; it willtherefore provide better geological lithofacies log curve estimations.

1. A system for inferring geological classes from downhole log datacomprising a neural network for inferring class probabilities,characterized in that said system further comprises means forintegrating class sequencing knowledge and optimising said classprobabilities according to said sequencing knowledge, and storage forsaid inferred geological classes to establish a relationship between theinferred geological classes and the downhole log data, wherein saidgeological classes comprise one of lithology, rock type andpetrophysical properties.
 2. The system of claim 1, wherein the meansfor integrating class sequencing knowledge and optimising said classprobabilities according to said sequencing knowledge comprises a hiddenMarkov model.
 3. An automated system for inferring geological classesfrom downhole log data, comprising a data input vector, a neural networktrained to infer from said input vector a class sequence or classprobability vector, and a modifier for correcting said class sequence orclass probability vector using prior knowledge of class sequence orclass probability, and storage for said inferred geological classes toestablish a relationship between the inferred geological classes and thedownhole log data, wherein said geological classes comprise one oflithology, rock type and petrophysical properties.
 4. An automatedsystem of claim 3, wherein the modifier uses the prior knowledge ofclass probability distribution and class transition probability.
 5. Anautomated system of claim 3, wherein the modifier includes a Viterbisequence optimisation.
 6. An automated system of claim 3, wherein themodifier includes a Bayesian based probability calculator.
 7. Anautomated system of claim 3, wherein the modifier includes a Bayesianbased probability calculator and a Viterbi sequence optimisation.
 8. Amethod for inferring geological classes from downhole log data,comprising the following steps: inferring class probabilities with aneural network; integrating class sequencing knowledge and optimisingsaid class probabilities according to said sequencing knowledge; andstoring said inferred geological classes to establish a relationshipbetween the inferred geological classes and the downhole log data,wherein said geological classes comprise one of lithology, rock type andpetrophysical properties.
 9. The method of claim 8, wherein theintegrating class sequencing knowledge and optimising said classprobabilities according to said sequencing knowledge is achievedaccording to a hidden Markov model.
 10. A method for inferringgeological classes from downhole log data, comprising the steps ofgenerating a data input based on said well input data; using a neuralnetwork to generate a class sequence or class probability vectorinferred from said input; correcting said class sequence or classprobability vector using prior knowledge of class sequence or classprobability; and storing said inferred geological classes to establish arelationship between the inferred geological classes and the downholelog data, wherein said geological classes comprise one of lithology,rock type and petrophysical properties.
 11. The method of claim 10,wherein prior knowledge of class probability distribution and classtransition probability is used to correct the class sequence or classprobability vector.
 12. The method of claim 10, wherein the correctionincludes a Viterbi sequence optimisation.
 13. The method of claim 10,wherein the correction includes a Bayesian based probabilitycalculation.
 14. The method of claim 10, wherein the correction includesa Bayesian based probability calculation and a Viterbi sequenceoptimisation.